How populations acquire beneficial mutations is of fundamental importance to evolutionary biology and to the treatment outcomes of diseases such as microbial infection and cancer. This renewal proposal, a collaboration between the Sherlock and Rosenzweig laboratories, will expand our understanding of adaptive evolution by using high-throughput sequencing in novel ways to gain the most granular view yet of the dynamics of a population of evolving cells. In the last funding period, we made fundamental discoveries concerning the dynamics and molecular nature of adaptive evolution and about epistatic interactions between beneficial mutations, using the model eukaryote S. cerevisiae (budding yeast). We propose here to extend these discoveries with major technological innovations that we are pioneering. The specific aims of this proposal are: 1) to measure the beneficial mutation rate and the distribution of fitness effects for the vast majority of beneficial mutations that will impact our evolving populations; 2) to map the adaptive landscape explored by that population through in-depth clonal and population sequencing; and 3) to determine how adaptive mutation rate and the distribution of fitness effects change in relation to ploidy, evolutionary history and environmental stress. For Aim 1, we have developed a molecular barcode-based lineage tracking system with which we can quantify, to high-resolution, the emergence and establishment of adaptive clones in evolving populations. This novel method greatly improves upon our previous fluorescence-based lineage tracking method: instead of tracking 3 subpopulations, we can now track half a million subpopulations, which we now refer to as lineages. We will use lineage tracking to estimate parameters that have been challenging to measure directly: the beneficial mutation rate and the distribution of selection coefficients for the vast majority of mutations affecting th course of evolution. These estimates will significantly advance evolutionary theory and enrich our understanding of all evolutionary processes driven by the accumulation of beneficial mutations. In Aim 2, we will sequence selected adaptive clones from our evolving populations, as well as the evolving populations themselves, obtaining detailed genome-wide data about what types of mutations provide an adaptive advantage and how the frequencies of novel beneficial alleles change over time. Finally, in Aim 3, we will use our lineage tracking system to investigate how beneficial mutation rate changes in relation to factors long thought to influence the dynamics of adaptive evolution: ploidy, evolutionary history and stress. Achieving these aims will enable us to see in unprecedented detail not only how beneficial mutations arise in yeast, but also how they arise in any mitotically-proliferating cell line subject to variation in ploidy, historical contingency and stress, including cancer.